LHSIR - Admirals

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Joueur	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP
1	Axel Jonsson-Fjallby		X			100.00	76	68	94	66	68	58	59	55	50	48	58	63	55	44	44	60	50	0	0
2	Deven Sideroff (R)			X		100.00	73	63	95	54	63	48	48	52	50	46	51	63	48	52	52	55	50	0	0
3	Lukas Jasek			X		100.00	74	68	88	61	68	71	75	58	50	51	61	63	58	44	44	63	50	0	0
4	Nicholas Caamano			X		100.00	79	44	89	63	72	55	78	54	44	58	58	64	25	45	45	62	50	0	0
5	Cody Glass (R)	X		X		100.00	63	41	94	80	67	65	61	74	53	64	64	59	25	47	47	66	50	0	0
6	Stefan Noesen		X	X		100.00	84	45	77	74	75	59	69	64	37	55	71	61	25	58	59	68	50	0	0
7	Taylor Raddysh (R)	X		X		100.00	83	80	89	63	80	78	83	60	75	53	62	68	59	44	44	65	50	0	0
8	Ryan MacInnis (R)	X				100.00	71	43	99	64	71	54	80	58	47	60	55	62	25	44	44	61	50	0	0
9	Jordy Bellerive (R)	X				100.00	72	69	78	67	69	74	79	56	70	49	59	61	56	44	44	61	50	0	0
10	Nick Ritchie		X			100.00	86	58	44	75	84	67	71	75	29	70	71	61	25	63	64	70	50	0	0
11	Ondrej Kase			X		100.00	59	41	91	86	68	74	61	73	33	75	64	62	75	59	60	68	50	0	0
12	Sasha Chmelevski (R)	X		X		100.00	73	68	84	65	68	65	66	62	78	59	62	63	59	44	44	64	50	0	0
13	Henri Jokiharju				X	100.00	76	43	86	82	67	71	84	61	25	52	48	75	25	57	57	63	50	0	0
14	Jacob Larsson				X	100.00	67	42	93	77	73	69	85	57	25	51	48	75	25	60	60	62	47	0	0
15	Lucas Carlsson				X	100.00	74	70	84	65	70	73	78	54	25	52	42	62	40	44	44	56	50	0	0
16	Michael Anderson (R)				X	100.00	76	71	89	77	71	63	67	49	25	41	41	61	39	44	44	54	50	0	0
17	Brogan Rafferty (R)				X	100.00	78	72	92	64	72	64	66	60	25	60	45	65	43	44	44	59	50	0	0
18	Leon Gawanke (R)				X	100.00	76	72	86	63	72	68	72	54	25	52	41	63	39	44	44	55	50	0	0
Rayé
1	Manuel Wiederer (R)	X		X		100.00	71	64	86	61	64	56	58	52	65	53	47	60	45	44	44	55	50	0	0
2	Nathan Noel (R)	X	X			100.00	64	63	66	53	63	49	51	44	55	38	44	54	42	44	44	47	50	0	0
3	Riley Sutter (R)			X		100.00	82	74	99	63	74	52	54	48	50	46	45	64	43	44	44	55	50	0	0
4	Keaton Thompson (R)				X	100.00	73	67	86	67	67	62	66	50	25	44	40	60	38	45	45	53	50	0	0
5	Noah Dobson (R)				X	100.00	69	42	91	80	71	62	63	69	25	53	48	59	25	47	47	60	50	0	0
6	Johnathan Kovacevic (R)				X	100.00	78	78	79	63	78	55	58	48	25	38	42	62	40	44	44	52	50	0	0
7	Mac Hollowell (R)				X	100.00	67	62	79	67	62	62	66	50	25	43	42	57	40	44	44	52	50	0	0

	MOYENNE D'ÉQUIPE					100.00	74	60	86	68	70	63	68	57	41	52	52	63	41	48	48	59	50	0

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Gardien	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP
1	Adam Werner (R)	100.00	57	59	74	73	59	61	51	60	58	57	30	44	44	57	50	0	0
2	Mikhail Berdin	100.00	59	62	78	63	60	63	51	61	58	57	30	44	44	58	50	0	0
Rayé

	MOYENNE D'ÉQUIPE	100.00	58	61	76	68	60	62	51	61	58	57	30	44	44	58	50	0

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Ryan Huska	66	70	73	68	65	60	82	CAN	46	3	850,000$

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Joueur	Nom de l'Équipe	POS	GP	G	A	P	+/-	PIM	PIM5	HIT	HTT	SHT	OSB	OSM	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PPM	PKG	PKA	PKP	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Sasha Chmelevski	Admirals (ANA)	C/RW	8	3	3	6	2	0	0	4	10	10	2	10	30.00%	1	119	14.95	0	1	1	0	32	0	0	0	0	0	0	0	40.00%	5	0	1.00	0	0	0	0	0	0	1	0
2	Stefan Noesen	Admirals (ANA)	LW/RW	8	4	1	5	2	10	0	11	5	18	5	12	22.22%	1	146	18.29	0	0	0	3	28	0	0	0	1	23	1	0	53.13%	32	1	0.68	0	1	0	0	0	1	0	0
3	Taylor Raddysh	Admirals (ANA)	C/RW	8	0	5	5	2	26	10	11	10	9	0	3	0.00%	0	111	13.91	0	1	1	3	30	0	0	0	0	0	0	0	45.54%	112	0	0.90	0	0	0	1	0	0	0	0
4	Leon Gawanke	Admirals (ANA)	D	8	0	5	5	6	22	10	13	5	1	0	1	0.00%	3	132	16.57	0	0	0	0	1	0	0	0	0	16	0	0	0.00%	0	0	0.75	0	0	1	0	1	1	0	0
5	Ondrej Kase	Admirals (ANA)	RW	8	2	2	4	-3	0	0	0	18	18	2	14	11.11%	0	182	22.85	1	1	2	6	35	0	0	0	1	48	0	0	37.38%	107	0	0.44	0	2	0	0	0	0	1	0
6	Brogan Rafferty	Admirals (ANA)	D	8	0	4	4	0	15	5	16	9	3	5	3	0.00%	6	167	20.99	0	1	1	2	29	0	1	1	0	34	0	0	0.00%	0	0	0.48	0	0	0	0	1	1	0	0
7	Henri Jokiharju	Admirals (ANA)	D	8	1	2	3	-3	10	0	26	11	6	4	7	16.67%	5	188	23.52	1	1	2	5	36	0	0	0	0	35	1	0	0.00%	0	0	0.32	0	0	0	0	0	0	0	1
8	Jacob Larsson	Admirals (ANA)	D	8	0	3	3	-3	8	0	6	8	4	0	2	0.00%	12	187	23.47	0	1	1	3	36	0	0	0	0	35	0	0	0.00%	0	0	0.32	0	0	0	0	0	0	0	0
9	Lucas Carlsson	Admirals (ANA)	D	8	1	2	3	-1	2	0	9	6	2	1	4	50.00%	6	175	21.90	1	0	1	2	30	0	1	1	0	38	0	0	0.00%	0	0	0.34	0	0	0	0	0	0	1	0
10	Cody Glass	Admirals (ANA)	C/RW	8	1	2	3	-3	0	0	1	16	10	4	12	10.00%	1	158	19.75	0	1	1	1	36	0	0	0	0	25	0	0	40.54%	148	0	0.38	0	1	0	0	0	0	0	0
11	Ryan MacInnis	Admirals (ANA)	C	8	3	0	3	1	4	0	4	11	6	0	6	50.00%	0	88	11.10	0	0	0	0	3	0	0	0	0	0	0	0	42.86%	77	0	0.68	0	0	0	0	0	0	0	2
12	Nick Ritchie	Admirals (ANA)	LW	8	2	1	3	-2	16	0	36	9	10	6	24	20.00%	1	168	21.11	1	1	2	2	35	1	0	1	1	35	0	1	42.86%	7	0	0.36	1	2	0	0	0	0	0	0
13	Deven Sideroff	Admirals (ANA)	RW	8	2	0	2	1	0	0	4	4	6	1	2	33.33%	1	77	9.68	0	0	0	0	5	0	0	0	0	0	1	0	44.44%	9	0	0.52	0	0	0	0	0	0	1	0
14	Lukas Jasek	Admirals (ANA)	RW	8	0	2	2	1	4	0	5	8	11	1	6	0.00%	0	63	7.98	0	0	0	0	1	0	0	0	0	0	0	0	0.00%	2	0	0.63	0	0	0	0	0	0	0	0
15	Michael Anderson	Admirals (ANA)	D	8	0	2	2	5	11	5	13	2	3	0	3	0.00%	7	122	15.37	0	0	0	0	3	0	0	0	0	1	0	0	0.00%	0	0	0.33	0	0	0	0	1	0	0	0
16	Jordy Bellerive	Admirals (ANA)	C	8	1	1	2	1	2	0	6	4	8	1	5	12.50%	0	61	7.74	0	0	0	0	0	0	0	0	0	0	0	0	68.25%	63	0	0.65	0	0	0	0	0	0	0	1
17	Axel Jonsson-Fjallby	Admirals (ANA)	LW	8	0	1	1	1	14	10	9	10	13	2	3	0.00%	0	91	11.50	0	0	0	0	0	0	0	0	0	8	0	0	60.00%	5	0	0.22	0	0	2	0	0	0	0	0
18	Nicholas Caamano	Admirals (ANA)	RW	8	0	1	1	2	10	0	8	6	9	3	11	0.00%	2	101	12.65	0	0	0	0	0	0	0	0	0	16	0	0	50.00%	10	0	0.20	0	0	0	0	0	0	0	0
Stats d'équipe Total ou en Moyenne				144	20	37	57	9	154	40	182	152	147	37	128	13.61%	46	2346	16.30	4	8	12	27	350	1	2	3	3	321	3	1	45.23%	577	1	0.49	1	6	3	1	3	3	4	4

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	GA	SA	PS %	PSA	ST	BG
1	Mikhail Berdin	Admirals (ANA)	8	3	3	2	0.865	2.76	457	21	156	0.429	7	8	0
2	Adam Werner	Admirals (ANA)	1	0	0	0	1.000	0.00	31	0	10	0.000	0	0	8
Stats d'équipe Total ou en Moyenne			9	3	3	2	0.873	2.58	489	21	166	0.429	7	8	8

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Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

Nom du Joueur	Nom de l'Équipe	POS	Âge	Date de Naissance	Nouveau Joueur	Poids	Taille	Non-Échange	Disponible pour Échange	Ballotage Forcé	Contrat	Type	Salaire Actuel	Salaire Restant	Salaire Moyen	Salaire Moyen Restant	Cap Salariale	Cap Salariale Restant	Exclus du Cap Salarial	Salaire Année 2	Salaire Année 3	Link
Adam Werner	Admirals (ANA)	G	23	1997-01-01	Yes	200 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Axel Jonsson-Fjallby	Admirals (ANA)	LW	22	1998-02-10	No	185 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Brogan Rafferty	Admirals (ANA)	D	25	1995-05-28	Yes	192 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Cody Glass	Admirals (ANA)	C/RW	21	1999-03-31	Yes	178 Lbs	6 ft2	No	No	No	3	Pro & Farm	1,713,333$	1,533,709$	1,713,333$	1,533,709$	0$	0$	No	1,713,333$	1,713,333$
Deven Sideroff	Admirals (ANA)	RW	23	1997-04-14	Yes	171 Lbs	5 ft11	No	No	No	3	Pro & Farm	935,833$	837,721$	935,833$	837,721$	0$	0$	No	935,833$	935,833$
Henri Jokiharju	Admirals (ANA)	D	21	1999-06-17	No	180 Lbs	6 ft0	No	No	No	3	Pro & Farm	925,000$	828,024$	925,000$	828,024$	0$	0$	No	925,000$	925,000$	Lien
Jacob Larsson	Admirals (ANA)	D	23	1997-04-29	No	195 Lbs	6 ft2	No	No	No	3	Pro & Farm	894,166$	800,423$	894,166$	800,423$	0$	0$	No	894,166$	894,166$	Lien
Johnathan Kovacevic	Admirals (ANA)	D	22	1997-07-12	Yes	207 Lbs	6 ft4	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Jordy Bellerive	Admirals (ANA)	C	21	1999-05-02	Yes	194 Lbs	5 ft11	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Keaton Thompson	Admirals (ANA)	D	24	1995-09-14	Yes	182 Lbs	6 ft0	No	No	No	3	Pro & Farm	925,000$	828,024$	925,000$	828,024$	0$	0$	No	925,000$	925,000$
Leon Gawanke	Admirals (ANA)	D	21	1999-05-31	Yes	198 Lbs	6 ft1	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Lucas Carlsson	Admirals (ANA)	D	22	1997-07-05	No	190 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Lukas Jasek	Admirals (ANA)	RW	22	1997-08-28	No	183 Lbs	6 ft1	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Mac Hollowell	Admirals (ANA)	D	21	1998-09-26	Yes	170 Lbs	5 ft10	No	No	No	3	Pro & Farm	799,766$	715,920$	799,766$	715,920$	0$	0$	No	799,766$	799,766$
Manuel Wiederer	Admirals (ANA)	C/RW	23	1996-11-21	Yes	170 Lbs	6 ft0	No	No	No	3	Pro & Farm	736,667$	659,436$	736,667$	659,436$	0$	0$	No	736,667$	736,667$
Michael Anderson	Admirals (ANA)	D	21	1999-05-25	Yes	196 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Mikhail Berdin	Admirals (ANA)	G	22	1998-02-28	No	163 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Nathan Noel	Admirals (ANA)	C/LW	23	1997-06-21	Yes	174 Lbs	5 ft11	No	No	No	3	Pro & Farm	925,000$	828,024$	925,000$	828,024$	0$	0$	No	925,000$	925,000$
Nicholas Caamano	Admirals (ANA)	RW	21	1998-10-07	No	194 Lbs	6 ft2	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Nick Ritchie	Admirals (ANA)	LW	24	1995-12-05	No	234 Lbs	6 ft2	No	No	No	3	Pro & Farm	1,498,925$	1,341,780$	1,498,925$	1,341,780$	0$	0$	No	1,498,925$	1,498,925$	Lien
Noah Dobson	Admirals (ANA)	D	20	2000-01-07	Yes	183 Lbs	6 ft4	No	No	No	3	Pro & Farm	1,431,667$	1,281,573$	1,431,667$	1,281,573$	0$	0$	No	1,431,667$	1,431,667$
Ondrej Kase	Admirals (ANA)	RW	24	1995-11-08	No	185 Lbs	6 ft0	No	No	No	3	Pro & Farm	2,600,000$	2,327,419$	2,600,000$	2,327,419$	0$	0$	No	2,600,000$	2,600,000$	Lien
Riley Sutter	Admirals (ANA)	RW	20	1999-10-24	Yes	200 Lbs	6 ft1	No	No	No	3	Pro & Farm	894,167$	800,424$	894,167$	800,424$	0$	0$	No	894,167$	894,167$
Ryan MacInnis	Admirals (ANA)	C	24	1996-02-13	Yes	185 Lbs	6 ft3	No	No	No	3	Pro & Farm	874,125$	782,483$	874,125$	782,483$	0$	0$	No	874,125$	874,125$
Sasha Chmelevski	Admirals (ANA)	C/RW	21	1999-06-09	Yes	187 Lbs	6 ft0	No	No	No	1	Pro & Farm	1,000,000$	895,161$	1,000,000$	895,161$	0$	0$	No			Lien
Stefan Noesen	Admirals (ANA)	LW/RW	27	1993-02-12	No	205 Lbs	6 ft1	No	No	No	3	Pro & Farm	450,000$	402,823$	450,000$	402,823$	0$	0$	No	450,000$	450,000$	Lien
Taylor Raddysh	Admirals (ANA)	C/RW	22	1998-02-18	Yes	216 Lbs	6 ft3	No	No	No	3	Pro & Farm	894,166$	800,423$	894,166$	800,423$	0$	0$	No	894,166$	894,166$

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
27	22.33	190 Lbs	6 ft1	2.11	1,055,475$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Nick Ritchie	Cody Glass	Ondrej Kase	35	1	2	2
2	Stefan Noesen	Taylor Raddysh	Sasha Chmelevski	30	1	2	2
3	Axel Jonsson-Fjallby	Ryan MacInnis	Nicholas Caamano	25	1	2	2
4	Deven Sideroff	Jordy Bellerive	Lukas Jasek	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jacob Larsson	Henri Jokiharju	35	1	2	2
2	Brogan Rafferty	Lucas Carlsson	30	1	2	2
3	Michael Anderson	Leon Gawanke	25	1	2	2
4	Jacob Larsson	Henri Jokiharju	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Nick Ritchie	Cody Glass	Ondrej Kase	60	1	2	2
2	Stefan Noesen	Taylor Raddysh	Sasha Chmelevski	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jacob Larsson	Henri Jokiharju	60	1	2	2
2	Brogan Rafferty	Lucas Carlsson	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Ondrej Kase	Nick Ritchie	60	1	2	2
2	Stefan Noesen	Cody Glass	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jacob Larsson	Henri Jokiharju	60	1	2	2
2	Brogan Rafferty	Lucas Carlsson	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1	Ondrej Kase	60	1	2	2	Jacob Larsson	Henri Jokiharju	60	1	2	2
2	Nick Ritchie	40	1	2	2	Brogan Rafferty	Lucas Carlsson	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1	Ondrej Kase	Nick Ritchie	60	1	2	2
2	Stefan Noesen	Cody Glass	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jacob Larsson	Henri Jokiharju	60	1	2	2
2	Brogan Rafferty	Lucas Carlsson	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Nick Ritchie	Cody Glass	Ondrej Kase	Jacob Larsson	Henri Jokiharju

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Nick Ritchie	Cody Glass	Ondrej Kase	Jacob Larsson	Henri Jokiharju

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Deven Sideroff, Ryan MacInnis, Nicholas Caamano	Deven Sideroff, Ryan MacInnis	Nicholas Caamano

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Michael Anderson, Leon Gawanke, Brogan Rafferty	Michael Anderson	Leon Gawanke, Brogan Rafferty

Tirs de Pénalité
Ondrej Kase, Nick Ritchie, Stefan Noesen, Cody Glass, Taylor Raddysh

Gardien
#1 : Mikhail Berdin, #2 : Adam Werner

Lignes d'Attaque Perso. en Prol.
, , , , , , , , , ,

Lignes de Défense Perso. en Prol.
, , , ,

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

	Ligue												Domicile											Visiteur
#	VS Équipe	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
1	Condors	2	1	1	0	0	0	0	0	5	6	-1	1	1	0	0	0	0	0	0	3	2	1	1	0	1	0	0	0	0	0	2	4	-2	2	0.500	5	9	14	0	0	8	3	9	1	29	38	59	48	8	50	14	43	51	9	0	0.00%	16	4	75.00%	1	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
2	Crunch	1	0	1	0	0	0	0	0	0	3	-3	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	3	-3	0	0.000	0	0	0	0	0	8	3	9	1	26	38	59	48	8	35	8	15	31	5	0	0.00%	5	0	100.00%	0	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
3	Monarchs	1	1	0	0	0	0	0	0	5	2	3	1	1	0	0	0	0	0	0	5	2	3	0	0	0	0	0	0	0	0	0	0	0	2	1.000	5	9	14	0	0	8	3	9	1	22	38	59	48	8	21	4	12	35	7	1	14.29%	6	1	83.33%	0	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
4	Moose	3	1	1	0	0	0	0	1	7	8	-1	1	0	1	0	0	0	0	0	2	3	-1	2	1	0	0	0	0	0	1	5	5	0	3	0.500	7	14	21	0	0	8	3	9	1	49	38	59	48	8	50	13	70	49	17	2	11.76%	18	2	88.89%	0	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
5	Sharks	1	0	0	0	0	0	0	1	3	4	-1	1	0	0	0	0	0	0	1	3	4	-1	0	0	0	0	0	0	0	0	0	0	0	1	0.500	3	5	8	0	0	8	3	9	1	21	38	59	48	8	10	7	14	16	4	1	25.00%	6	0	100.00%	0	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
	Total	8	3	3	0	0	0	0	2	20	23	-3	4	2	1	0	0	0	0	1	13	11	2	4	1	2	0	0	0	0	1	7	12	-5	8	0.500	20	37	57	0	0	8	3	9	1	147	38	59	48	8	166	46	154	182	42	4	9.52%	51	7	86.27%	1	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
	_Since Last GM Reset	8	3	3	0	0	0	0	2	20	23	-3	4	2	1	0	0	0	0	1	13	11	2	4	1	2	0	0	0	0	1	7	12	-5	8	0.500	20	37	57	0	0	8	3	9	1	147	38	59	48	8	166	46	154	182	42	4	9.52%	51	7	86.27%	1	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
	_Vs Conference	7	3	2	0	0	0	0	2	20	20	0	4	2	1	0	0	0	0	1	13	11	2	3	1	1	0	0	0	0	1	7	9	-2	8	0.571	20	37	57	0	0	8	3	9	1	121	38	59	48	8	131	38	139	151	37	4	10.81%	46	7	84.78%	1	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
	_Vs Division	7	3	2	0	0	0	0	2	20	20	0	4	2	1	0	0	0	0	1	13	11	2	3	1	1	0	0	0	0	1	7	9	-2	8	0.571	20	37	57	0	0	8	3	9	1	121	38	59	48	8	131	38	139	151	37	4	10.81%	46	7	84.78%	1	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50

			Total Pour les Joueurs
Matchs Joués	Points	Séquence	Buts	Passes	Points	Tirs Pour	Tirs Contre	Tirs Bloqués	Minutes de Pénalité	Mises en Échec	Buts en Filet Désert	Blanchissage
8	8	L2	20	37	57	147	166	46	154	182	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
8	3	3	0	0	0	2	20	23

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	2	1	0	0	0	1	13	11

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
4	1	2	0	0	0	1	7	12

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
3	3	0	0	0	2

Tentatives en Avantage Numérique	Buts en Avantage Numérique	% en Avantage Numérique	Tentatives en Désavantage Numérique	Buts Contre en Désavantage Numérique	% en Désavantage Numérique	Buts Pour en Désavantage Numérique
42	4	9.52%	51	7	86.27%	1

Tirs en 1e Période	Tirs en 2e Période	Tirs en 3e Période	Tirs en 4e Période	Buts en 1e Période	Buts en 2e Période	Buts en 3e Période	Buts en 4e Période
38	59	48	8	8	3	9	1

Mises en Jeu
Gagnées en Zone Offensive	Total en Zone Offensive	% Gagnées en Zone Offensive	Gagnées en Zone Défensive	Total en Zone Défensive	% Gagnées en Zone Défensive	Gagnées en Zone Neutre	Total en Zone Neutre	% Gagnées en Zone Neutre
106	212	50.00%	97	234	41.45%	58	131	44.27%

Temps Avec la Rondelle
En Zone Offensive	Contrôle en Zone Offensive	En Zone Défensive	Contrôle en Zone Défensive	En Zone Neutre	Contrôle en Zone Neutre
189	127	199	62	101	50

Derniers Match Joués Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
1	`\|` or `OR`	Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2	`&&` or `AND`	Logical "and". Filter the column for content that matches text from either side of the operator.
3	`/\d/`	Add any regex to the query to use in the query ("mig" flags can be included `/\w/mig`)
4	`< <= >= >`	Find alphabetical or numerical values less than or greater than or equal to the filtered query
5	`!` or `!=`	Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (`=`), single (`'`) or double quote (`"`) to exactly not match a filter.
6	`"` or `=`	To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7	`-` or `to`	Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8	`?`	Wildcard for a single, non-space character.
8	`*`	Wildcard for zero or more non-space characters.
9	`~`	Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10	text	Any text entered in the filter will match text found within the column

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	Lien
1 - 2020-09-27	11	Admirals	0	Crunch	3	L			Sommaire du Match
2 - 2020-09-28	19	Monarchs	2	Admirals	5	W			Sommaire du Match
4 - 2020-09-30	27	Sharks	4	Admirals	3	L	X	X	Sommaire du Match
5 - 2020-10-01	42	Condors	2	Admirals	3	W			Sommaire du Match
6 - 2020-10-02	52	Admirals	3	Moose	4	L	X	X	Sommaire du Match
8 - 2020-10-04	62	Admirals	2	Moose	1	W			Sommaire du Match
10 - 2020-10-06	68	Admirals	2	Condors	4	L			Sommaire du Match
12 - 2020-10-08	79	Moose	3	Admirals	2	L			Sommaire du Match
14 - 2020-10-10	89	Admirals	-	Monarchs	-
17 - 2020-10-13	107	Moose	-	Admirals	-
18 - 2020-10-14	117	Admirals	-	Flames	-
20 - 2020-10-16	128	Admirals	-	Stars	-
21 - 2020-10-17	131	Flames	-	Admirals	-
23 - 2020-10-19	150	Condors	-	Admirals	-
24 - 2020-10-20	161	Admirals	-	Marlies	-
25 - 2020-10-21	169	Sharks	-	Admirals	-
26 - 2020-10-22	175	Admirals	-	Condors	-
28 - 2020-10-24	192	Admirals	-	Rampage	-
30 - 2020-10-26	198	Wolf Pack	-	Admirals	-
32 - 2020-10-28	210	Admirals	-	Flames	-
34 - 2020-10-30	222	Monsters	-	Admirals	-
36 - 2020-11-01	237	Rocket	-	Admirals	-
38 - 2020-11-03	249	Admirals	-	Marlies	-
40 - 2020-11-05	253	Admirals	-	IceHogs	-
42 - 2020-11-07	265	Admirals	-	Flames	-
43 - 2020-11-08	271	Monarchs	-	Admirals	-
45 - 2020-11-10	285	Marlies	-	Admirals	-
46 - 2020-11-11	300	Flames	-	Admirals	-
47 - 2020-11-12	305	Admirals	-	Moose	-
49 - 2020-11-14	324	Monarchs	-	Admirals	-
51 - 2020-11-16	335	Condors	-	Admirals	-
52 - 2020-11-17	345	Admirals	-	Condors	-
53 - 2020-11-18	358	Admirals	-	Monarchs	-
54 - 2020-11-19	367	Condors	-	Admirals	-
55 - 2020-11-20	378	Admirals	-	Condors	-
57 - 2020-11-22	390	Wolf Pack	-	Admirals	-
58 - 2020-11-23	401	Admirals	-	Senators	-
59 - 2020-11-24	410	Senators	-	Admirals	-
60 - 2020-11-25	422	Bruins	-	Admirals	-
63 - 2020-11-28	434	Admirals	-	Griffins	-
64 - 2020-11-29	446	Sound Tigers	-	Admirals	-
65 - 2020-11-30	456	Admirals	-	Rocket	-
67 - 2020-12-02	467	Admirals	-	Sound Tigers	-
68 - 2020-12-03	475	Admirals	-	Sharks	-
69 - 2020-12-04	486	Phantoms	-	Admirals	-
71 - 2020-12-06	497	Monarchs	-	Admirals	-
72 - 2020-12-07	511	Sharks	-	Admirals	-
74 - 2020-12-09	525	Admirals	-	Stars	-
75 - 2020-12-10	534	Sharks	-	Admirals	-
77 - 2020-12-12	546	Admirals	-	Sharks	-
78 - 2020-12-13	554	Admirals	-	Soldiers	-
79 - 2020-12-14	566	Soldiers	-	Admirals	-
81 - 2020-12-16	582	Monarchs	-	Admirals	-
82 - 2020-12-17	588	Admirals	-	Moose	-
84 - 2020-12-19	601	Crunch	-	Admirals	-
86 - 2020-12-21	611	Admirals	-	Flames	-
87 - 2020-12-22	625	Moose	-	Admirals	-
88 - 2020-12-23	636	Admirals	-	Soldiers	-
90 - 2020-12-25	644	Admirals	-	Monsters	-
91 - 2020-12-26	654	Penguins	-	Admirals	-
92 - 2020-12-27	662	Admirals	-	IceHogs	-
94 - 2020-12-29	677	Wolves	-	Admirals	-
95 - 2020-12-30	687	Flames	-	Admirals	-
96 - 2020-12-31	699	Admirals	-	Penguins	-
97 - 2021-01-01	707	Admirals	-	Phantoms	-
99 - 2021-01-03	719	IceHogs	-	Admirals	-
100 - 2021-01-04	731	Admirals	-	Bruins	-
102 - 2021-01-06	742	Flames	-	Admirals	-
103 - 2021-01-07	755	Griffins	-	Admirals	-
104 - 2021-01-08	763	Admirals	-	Marlies	-
105 - 2021-01-09	777	Wolves	-	Admirals	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
107 - 2021-01-11	787	Admirals	-	Wolf Pack	-
108 - 2021-01-12	800	Rampage	-	Admirals	-
109 - 2021-01-13	805	Admirals	-	Sharks	-
111 - 2021-01-15	821	Rampage	-	Admirals	-
112 - 2021-01-16	824	Admirals	-	Wolves	-
114 - 2021-01-18	843	Admirals	-	Monarchs	-
116 - 2021-01-20	853	Stars	-	Admirals	-
117 - 2021-01-21	866	Admirals	-	Monarchs	-
118 - 2021-01-22	873	Moose	-	Admirals	-
121 - 2021-01-25	890	Marlies	-	Admirals	-
122 - 2021-01-26	897	Admirals	-	Sharks	-

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité de l'Aréna	2000	1000
Prix des Billets	35	15
Assistance	0	0
Assistance PCT	0.00%	0.00%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité de l'Aréna	Popularité de l'Équipe
37	0 - 0.00%	0$	0$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
387,881$	2,849,782$	2,849,782$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	298,766$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
0$	111	29,837$	3,311,907$

Afficher ou Masquer des Colonnes

	Ligue											Domicile											Visiteur
Année	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	GP	W	L	T	OTW	OTL	SOW	SOL	GF	GA	Diff	P	G	A	TP	SO	EG	GP1	GP2	GP3	GP4	SHF	SH1	SP2	SP3	SP4	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	PK GA	PK%	PK GF	W OF FO	T OF FO	OF FO%	W DF FO	T DF FO	DF FO%	W NT FO	T NT FO	NT FO%	PZ DF	PZ OF	PZ NT	PC DF	PC OF	PC NT
2020	8	3	3	0	0	0	0	2	20	23	-3	4	2	1	0	0	0	0	1	13	11	2	4	1	2	0	0	0	0	1	7	12	-5	8	20	37	57	0	0	8	3	9	1	147	38	59	48	8	166	46	154	182	42	4	9.52%	51	7	86.27%	1	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50
Total Saison Régulière	8	3	3	0	0	0	0	2	20	23	-3	4	2	1	0	0	0	0	1	13	11	2	4	1	2	0	0	0	0	1	7	12	-5	8	20	37	57	0	0	8	3	9	1	147	38	59	48	8	166	46	154	182	42	4	9.52%	51	7	86.27%	1	106	212	50.00%	97	234	41.45%	58	131	44.27%	189	127	199	62	101	50